Background

Multidisciplinary Cancer Conferences (MCCs) are regular meetings held by cancer specialists to prospectively discuss treatment plans for patients with cancer. MCCs typically include representation by surgeons, medical and radiation oncologists, pathologists and radiologists, and may also include nurses and other allied professionals. MCCs strengthen collaboration and communication among participating physicians, increase adherence to clinical guidelines, decrease wait times to treatment implementation, and improve multidisciplinary collaboration among cancer specialists [1,2,3]. Despite the importance of MCCs to the quality of care received by patients with cancer, numerous barriers to optimal MCC functioning have been described including gaps in case organization, teamworking, leadership and technology [3, 4]. Despite these identified gaps, there has been little research on the impact of interventions to improve the quality of MCC functioning. A concerted effort in one jurisdiction in the United Kingdom led to only slight improvements in MCC decision making quality [5, 6].

In 2010 in Ontario, Canada, Cancer Care Ontario (CCO), the agency responsible for promoting quality care for patients with cancer in the province, mandated the use of MCCs in hospitals. CCO set a target that required at least 80% of hospitals in the province to meet the required provincial MCC standards by 2015 [7]. A secondary goal was to double the volume of cases discussed by the 2015 deadline (from 24,000 cases in 2010 to 50,000 cases in 2015). The province met these goals, and by 2015, 80% of hospitals (51/64 hospitals) met the minimum CCO quality criteria. Recent data also show a further increase in MCC standard concordance among non-regional cancer center hospitals, with 85% of these hospitals having met CCO quality criteria [8].

However, there remain two challenges associated with MCC functioning: inadequate access to MCCs and lack of quality assessment of MCCs. Ontario data suggests that less than half of all new cancer cases are discussed prospectively in a multidisciplinary forum [8]. Resource barriers, specifically limited review time coupled with high case volumes, preclude MCC teams from discussing all new or suspected cancer cases. For instance, a study by our group demonstrated that only 20% of thoracic cancer cases at a leading academic institution were discussed at a MCC [9]. Second and perhaps more importantly, and similar to other jurisdictions in the world, there has been no formal retrospective or prospective evaluation of MCC decision making quality at the individual case or round/session level. Evaluations of MCC quality in Ontario have been limited to the types of specialists in attendance and MCC frequency. Such measures do not interrogate the quality of decision making or resulting treatment recommendations. Previous research by our group evaluating MCC decision making quality in a limited setting found gaps in the quality of information presented and the quality of MCC teamworking [10]. Via an evaluation of two MCCs, we identified variation in amount and quality of contribution by specialty, gaps in leadership, and little consideration of patient views during MCC discussion [10]. Informed by these data, we aimed to use Knowledge Translation (KT) methods to determine the barriers and facilitators to optimal MCC decision making and to identify potentially effective solutions to mitigating and leveraging these barriers and facilitators, respectively.

KT is the process of developing, disseminating and applying evidence to improve processes and outcomes. Examples of KT interventions (or implementation strategies) include the use of audit and feedback, incentives, educational meetings, and team training [11,12,13,14,15]. KT interventions are believed to be most effective if they first identify the determinants of behaviour change, and subsequently identify strategies that target these determinants [16]. To facilitate this, KT experts have increasingly called for the use of theoretical frameworks and models.

For this study, we were interested in developing a KT strategy that could potentially improve the quality of MCC decision making in an Ontario context. We report herein key results and observations. The intended audiences for this work include implementation scientists, researchers and clinician scientists interested in the pragmatic application of KT methodology and multidisciplinary cancer teams interested in the barriers, facilitators and potential solutions to improve MCC decision making.

Methods

Theoretical underpinnings

The Theoretical Domains Framework (TDF) and Capabilities-Opportunities-Motivation (COM-B) Behaviour Change Wheel were used to guide this study [17, 18]. The TDF can be used to identify factors impacting the uptake of a behaviour of interest, while the COM-B facilitates mapping of these factors corresponding interventions. There are only a few examples of studies that describe use of the TDF and COM-B to design KT interventions [19, 20].

The use of theory in intervention design is important, as it allows for the systematic mapping of behavioural constructs to corresponding KT intrventions. However, given the many theories of behaviour change, it is difficult to justify the prioritization of one theory over another. One benefit of the TDF is that it is a meta-framework that incorporates 33 psychology theories and over 128 behavioural change constructs. Additionally, the TDF can be directly transposed on to the COM-B Behaviour Change Wheel, thereby removing much of the ‘guesswork’ required to determine corresponding implementation strategies.

Study design

We used a multi-staged approach to design the KT-MCC Strategy. First, key informant interviews based on the TDF were conducted with Ontario MCC participants to identify factors to optimal MCC decision making. We defined ‘optimal decision making’ as encompassing best practices for MCC case presentation, discussion and final selection of treatment plans. Factors were then linked to barrier and facilitator TDF-domains. Second, the COM-B model facilitated the mapping of identified TDF-domains to specific KT interventions, to create our prototype KT-MCC Strategy [18, 20]. Finally, focus groups with MCC participants were held to a) confirm the clinical relevance of the key informant data (i.e., trustworthiness); b) identify potential additional barriers and facilitators to optimal MCC decision making; and c) consider the acceptability and appropriateness of the proposed KT-MCC Strategy [21].

This study was approved by the Hamilton Integrated Ethics Review Board.

Setting

The province of Ontario, Canada (population 13 million) is comprised of 14 geographic Local Health Integrated Networks (LHINs). Relevant stakeholders from two LHINs were sampled for this study. These two LHINs contain academic and community hospitals and collectively service a population of 2.5 million Ontarians, or approximately 20% of residents in the province.

Part 1 – key informant interviews

Interview guide

The TDF includes 14 domains that may influence a particular behavior of interest. These domains include: Knowledge; Skills; Social/Professional Role and Identity; Beliefs about Capabilities; Optimism; Beliefs about Consequences; Reinforcement; Intentions; Goals; Memory, Attention and Decision Processes; Environmental Context and Resources; Social Influences; Emotion; and Behavioural Regulation [17].

We used relevant MCC literature to design content-specific questions relating to each of the 14 TDF domains. A content expert was consulted to ensure face validity of the interview guide (see Additional File 1). We used semi-structured interviewing which is recommended for interviewing participants in a single interview [22]. It allows the researcher to pursue probing questions to gain further context and ensure a comprehensive understanding of the key informants’ experiences or perceptions. The interview guide was modeled after examples provided in the implementation science literature [23,24,25,26].

Study procedure and participants

MCC standards outlined by CCO identify the following individuals as core members of the MCC: designated MCC chair and coordinator, and representatives from medical oncology, radiation oncology, surgery, pathology and radiology. Participation by nursing is encouraged, although not mandated, and was less prevalent at the study sites. MCC chairs, surgeons, medical and radiation oncologists, pathologists and radiologists were therefore prioritized as the target population for the key informant interviews. Participants were recruited using purposeful and snowball sampling. Participants were emailed and invited to participate in either a face-to-face or telephone key informant interview. An experienced qualitative researcher trained in the use of the TDF conducted all interviews. Following participant consent, all interviews were recorded and transcribed verbatim. We ensured participant representation by specialist type (surgeons, oncologists, radiologists & pathologists) to allow for a comparison of findings by specialist group.

Sample size was determined using Francis et al.’s framework for theoretically-rooted interviewing, which sets an a priori minimum sample size of 10, with a ‘stopping criterion’ when three interviews do not identify new themes (i.e., minimum of ≥13 interviews, given the last three interviews reach data saturation) [27].

Analysis

Deductive and inductive coding were used. First, a coding tree with context-specific definitions for each of the 14 TDF domains was developed and used to guide the deductive coding. Two reviewers then organized the interview data using the TDF domains. Next, open-coding was used to identify salient themes within each of the TDF domains. Themes are qualitative descriptions of interview findings that further provide context to the TDF domains. The decision to present the findings as both TDF domains and emergent themes was made to provide context to the theoretical findings and ensure that the key informant data were meaningful to the target population [28].

Two researchers first jointly analyzed three interviews to ensure consistency in coding. The two researchers double-coded the remainder of the key informant interviews. In instances where discrepancies in coding arose, the researchers discussed the differences in order to reach consensus. If a quote was found to fit into multiple themes or domains following discussion, it was double coded. Double coding allowed our research team to identify linked themes and related domains. The decision to double code the qualitative data is consistent with evidence that some TDF domains measure a combination of factors and do not represent an isolated behaviour [29, 30]. Salient quotes were highlighted to provide “thick descriptions” of participants’ experiences in MCCs. Finally, data were analyzed by physician specialist group.

Part 2 – TDF to COM-B mapping process

The COM-B integrates 19 behavioural change frameworks into three facets of behavior change – capabilities, opportunities and motivation [18]. At the core of the COM-B are six sources of behaviour that affect the capabilities, opportunities or motivation needed for behaviour change including social and physical opportunity, automatic and reflective motivation, and psychological and physical capability [18]. These sources are linked to nine intervention functions (e.g., education, training) that can be used to potentially alter the behaviour. At the outer edge of the wheel are policy categories (e.g., environmental/social planning, legislation) that promote organizational change [18]. The intent of the COM-B is to facilitate the selection of KT interventions that are most likely to overcome identified barriers to behaviour change.

TDF domains identified as mediators to MCC decision making were mapped to the COM-B to first identify the facet of behaviour (capability, opportunity or motivation) requiring intervention. Then, corresponding intervention functions for each identified facet of behaviour were identified. Evidence regarding the efficacy of these intervention functions (gathered via a review of the Effective Practice and Organization of Care strategies, systematic reviews and evidence on teamworking from the business literature) was used to operationalize the intervention functions [13,14,15]. The interventions were further refined following discussions with focus group participants and presented as the first iteration of the KT-MCC Strategy [31].

Part 3 – focus groups

Focus group participants

Three specialty-specific focus groups were held. The first focus group involved surgeons, the second involved medical and radiation oncologists, and the third involved radiologists and pathologists. Each focus group included a MCC coordinator. The MCC coordinator (who is sometimes a nurse or nurse practitioner) is responsible for organizing all MCC data and imaging prior to discussion, and to record the subsequent treatment recommendations. Focus groups took place at a single academic hospital site. Participants took part in either the key informant interviews or focus group discussions.

Study procedure

Focus groups with MCC participants were held to confirm the qualitative validity of the key informant data and to identify potential additional barriers and facilitators to optimal MCC decision making not identified in the key informant interviews. Qualitative validity pertains to the accuracy and relevance of findings, and is often referred to as trustworthiness or confirmability [32,33,34,35]. Focus group participants also considered the acceptability and appropriateness (as defined by Proctor et al.) of the prototype KT-MCC Strategy [21]. Insights obtained from the focus group findings were used as needed to modify the KT-MCC Strategy.

Data analysis

All focus group data were recorded and transcribed verbatim. Data were analyzed thematically using a compare and contrast method on Nvivo software. In this method, themes are compared in order to identify similarities and differences [36, 37].

Reflexivity statement

The primary author was trained in KT methodology and led the design of the discussion guides, under the guidance of MS and RRS. The primary author (CF) conducted the interviews and focus groups and holds extensive experience in these methods. CF and AA independently coded the qualitative data. Both researchers were trained in qualitative methodology and conducted the research as graduate students. The researchers did not hold a relationship with the participants prior to the key informant interviews or focus groups.

Results

Participant demographics – key informant interviews

Interviews were conducted from April 2016 – July 2017. Data saturation was reached at n = 21 interviews. Participants included seven surgeons, eight oncologists (five medical oncologists, three radiation oncologists), three radiologists and three pathologists. Participants were from three academic and four community sites. Participants reported attending MCCs for gastrointestinal (n = 14), hepatobiliary (n = 7), lung (n = 7), breast (n = 3), neuroendocrine (n = 2), genitourarinary (n = 2), sarcoma (n = 1), and gynecologic (n = 1) disease. Many participants attended multiple MCCs. The majority of interviews (17/21) were conducted in person, while the remainder (4/21) were conducted via telephone. Mean time per interview was 33 min (range 19 min – 78 min).

Barriers to optimal MCC decision making

Twenty-seven themes were identified as barriers and ascribed to 11/14 TDF domains. Most domains contained 2–4 qualitative themes (see Table 1).

Table 1 Barriers to Optimal MCC Decision Making
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Participants most commonly described barriers relating to MCC processes. For instance, participants identified gaps in MCC leadership, lack of core participant attendance, lack of MCC participant preparation, unstructured processes for case presentation, individual treatment preferences by treating physicians, and prolonged case discussions (see Table 1 for sample quotes). These themes interacted to impede MCC decision making. One participant stated,

“[MCCs] get frustrating because the discussion goes on way past the decision making point and we get into ‘at nauseum’ discussions where there cannot be a black and white answer”- Participant 8 (Surgeon)

Participants also commonly described themes relating to social dynamics that negatively impact MCC decision making including a lack of soft skills (e.g., use of non-effective communication strategies) among group members, negative group dynamics or bullying, the inability to ask questions openly, and the domination of the conversation by few individuals.

Other identified themes included a lack of: awareness of guidelines outlined by CCO regarding MCC processes, clinical knowledge required to meaningfully inform cases, and buy in regarding MCCs by certain physicians/specialty groups. Some participants also reported hierarchical group dynamics between academic and community sites, which impacted decision making. Contextual factors including limited time, administrative supports and physical resources (e.g., access to imaging) were also found to impede decision making. Such themes were often tied closely to barriers categorized in the Emotion domain, which included feelings of under-appreciation and conflict during the decision making process. For instance, feelings of under-appreciation arose when a MCC participant felt they had dedicated significant time to prepare for an MCC case, and their colleagues had not reciprocated in kind. This is best explained in the following quote by a radiologist who highlights the interaction between time demands and feelings of under-appreciation,

“Nobody, honestly, has the appreciation of the amount of time … we [radiologists] put in those rounds and the time it takes … they [surgeons] ask us to prepare for those rounds. I think it’s unacceptable that they’re not prepared.” – Participant 3 (Radiologist).

Lack of attendance by presenting physicians (oncologists or surgeons), and time barriers often led to a cancellation of cases listed for discussion. This further caused feelings of frustration for radiologists and pathologists (who had prepared the case imaging and slides), and other participants who were in attendance at time of MCC discussion. Additional themes are outlined in Table 1.

Identified barriers by specialist group

While some barriers were common to all specialists (e.g., time demands), others varied by specialist group. For instance, surgeons identified scheduling conflicts and operating room demands as barriers to regular MCC attendance. Surgeons’ level of participation in MCCs varied according to their intrinsic beliefs regarding the importance of collaborative decision making (i.e., those who valued collaborative decision making were more likely to regularly participate in MCCs). Moreover, surgeons reported that surgeon hierarchy, technical ability, and group dominance often influenced the final surgical recommendation.

Unlike surgeons, there was less variation in oncologists’ opinions regarding the role of MCCs, and most oncologists cited MCCs as an integral part of their practice. The most common barrier cited by oncologists was the inability to make a decision due to limited evidence and conflicting treatment recommendations. For instance, one medical oncologist highlighted being “quite confused” when surgeons and radiation oncologists presented conflicting treatment decisions. Oncologists further highlighted lack of leadership led to cyclical discussions without a clearly articulated final plan, as seen in the following quote,

“The facilitator tends to just let people talk in circles over and over again for ten minutes about the same thing and there’s no directing the discussion to ‘Okay, let’s kind of get back on course to this patient … let’s kind of, you know, circle back to what do we need to do for this patient now.’”- Participant 16 (Medical Oncologist).

Pathologists and radiologists described the significant amount of time required to prepare for MCCs as a barrier and highlighted that MCC preparation adds greatly to their regular workload. For instance, preparation of radiologic or pathologic findings for a MCC can take up to one full work-day to prepare. These time barriers led to subsequent feelings of frustration and under-appreciation when other treating physicians did not adequately contribute the required time to prepare for, and attend, MCCs.

Facilitators to optimal MCC decision making

Thirteen themes ascribed to 10/14 TDF domains were identified as facilitators to optimized MCC decision making. As expected, the converse of many barrier themes were identified as facilitators. For example, physician knowledge of available clinical guidelines and recent evidence facilitated clinicians’ ability to make a treatment decisions; however, the absence of this knowledge was a barrier to decision making.

A summary of all identified domains and corresponding themes are listed in Table 2. The most commonly reported facilitators to optimized MCC decision-making included opportunities for learning and standardization of decision making and facilitated collegiality and teamworking. Mandated MCCs and buy-in from leadership were also facilitators in this context.

Table 2 Facilitators to Optimal MCC Decision Making
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The TDF domains of Skills, Intentions, and Goals were identified uniquely as facilitators to MCC processes. Participants reported that regular attendance and participation in MCC rounds increased their knowledge and skills, and allowed them to gain an appreciation of their colleagues’ decision making practices. For example, pathologists highlighted that participation in MCCs equipped them to better present histological findings to treating physicians, while simultaneously promoting a collaborative culture of interdisciplinary teamwork. Motivations to engage in MCCs stemmed from an intrinsic affinity to quality improvement. Intrinsic motivation and general optimism towards MCCs were suggested to be more effective than external reinforcements/penalties.

Additional motivators to optimize MCC decision making quality included patient preference to have their case discussed by a multidisciplinary team, a desire to collaborate with peers, and increased confidence in the final treatment decisions. Many participants described the importance of streamlined MCC processes (e.g., attendance schedule and a triage system for urgent cases) to facilitate decision making. Recommendations on operationalization of these processes differed based on the culture and needs of the individual team.

Identified Facilitators by Specialist Group

We did not identify any major significant differences in identified MCC facilitators by specialist groups. Rather, MCC participants generally spoke favorably of MCCs, and noted their importance to patient care, as demonstrated in the following quote,

“[MCCs] are absolutely vital. I can’t imagine having a centre [or] any area where there’s multidisciplinary care of patients, where you’re not getting together to discuss the difficult cases” – Participant 14 (Radiation Oncologist).

Some radiologists suggested that they did not receive the same level of monetary compensation as other MCC participants. However, reimbursement inequalities did not appear preclude radiologists and pathologists from regularly attending and participating in MCCs, which further reinforces the notion that intrinsic motivation is likely the main catalyst to MCC participation.

Corresponding interventions to barriers and facilitators

The COM-B mapping exercise revealed that all six sources of behaviour change on the behaviour change wheel (psychological and physical Capabilities; social and physical Opportunities; automatic and reflective Motivation) could benefit from potential intervention. Based on these data, we determined that a multipronged intervention was required to improve the quality of MCC decision making. Table 3 outlines TDF domains identified as barriers or facilitators to MCC decision making and depicts the corresponding COM-B behavioural facet and intervention functions. Identified COM-B intervention functions to improve MCC decision making included modeling, environmental restructuring, restrictions, education, persuasion, incentivisation, coercion, enablement, and training. Table 4 presents the evidence and rationale that guided the formation of pragmatic implementation strategies.

Table 3 TDF Domains Mapped to COM-B
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Table 4 KT-MCC Strategy – Intervention Components
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The resulting KT-MCC Strategy interventions derived from the mapping exercise included workshops, chair training, team training, standardized intake forms and synoptic checklist, and, audit and feedback [17, 18]. These strategies are further described in Table 4.

Participant demographics – focus group

A total of n = 18 MCC participants took part in the focus groups (n = 5 surgeons; n = 6 oncologists; n = 7 radiologists and pathologists). Participants reported attending MCCs for gastrointestinal, hepatobiliary, genitourinary, lung, breast, and sarcoma disease.

Table 5 compares all key informant and focus group data. All key informant themes were confirmed by at least some focus group participants, and the focus groups did not identify any new barrier or facilitator themes. Consistent with the key informant data, focus group participants generally held positive attitudes regarding the benefits of MCCs (e.g., improved collegiality, timely development of treatment plans, and reassurance for the management of complex cases). They did not perceive incentives, such as billing or continued medical education (CME) credits, to be significant motivators to their MCC engagement. Barriers identified through the key informant interviews were confirmed.

Table 5 Key Informant and Focus Group Data
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While the validity of all themes were confirmed by some focus group participants, there were themes that did not generate consensus within and between focus groups. Table 5 demonstrates the differences in reported barriers and facilitators by focus group. For example, key informants identified the presence of bullying as a barrier. While some surgeon participants presented first-hand examples of when they were bullied, or treated poorly at rounds, others rejected the notion of bullying within their MCC team. Pathologists and radiologists did not report being personally bullied, but witnessed other treating physicians falling victim to negative group dynamics. In contrast, oncologists did not confirm this theme and did not perceive bullying or negative team dynamics to be a significant problem to MCC decision making.

Acceptability and appropriateness of KT-MCC strategy

The final study objective was to present the KT-MCC intervention components (workshops, team training, chair training, standard intake forms and synoptic checklist, and audit and feedback) to focus group participants to determine perceived acceptability and appropriateness. All groups identified chair training as an acceptable and appropriate intervention, as demonstrated in the following participant quote:

“I think having an effective chair that helps promote flow and efficiency is very helpful. These things [leading a discussion] are not necessarily things that are intuitive.”

Participants were also generally positive towards audit and feedback, use of a standard intake form, and the use of workshops to develop local consensus processes (i.e., ‘rules of functioning’ for MCCs). However, while they confirmed the appropriateness of team training and use of a synoptic discussion checklist (i.e., if implemented, would likely have a positive impact on MCC decision making) they were divided on whether the interventions were acceptable (i.e., ‘palatable’ or desirable). Some participants highlighted concerns that a synoptic discussion checklist would reduce the efficiency of a case discussion, and that limited time resources would preclude team training attendance, as demonstrated in the following quote,

“Nobody is going to go (to a team training session). You can set it up and I don’t know if anybody would show up.”

These perceptions were not consistent among team members and did not correlate with physician specialty group. For instance, other participants suggested that a team training session would improve soft-skills and team communication and would thus likely improve MCC efficiency.

Discussion

TDF-rooted interviews identified numerous barriers and facilitators to optimal MCC decision-making. Identified themes were linked to TDF domains and then mapped to the COM-B to develop the first iteration of the KT-MCC Strategy which included workshops, team training, chair training, use of a standard intake form and synoptic discussion checklist, and, audit and feedback. Focus groups confirmed the validity of the key informant data and acceptability of the proposed KT-MCC Strategy interventions.

There are few examples in the KT literature that provide a complete and detailed description of how to design a complex KT strategy using the TDF and COM-B [48]. Even fewer studies evaluate the impact of such interventions on process or patient outcomes. As a result, there are limited examples in the implementation science literature to guide operationalization of the TDF and COM-B to develop pragmatic interventions. Existing examples include a comprehensive analysis by Alexander et al. using the TDF and COM-B to identify barriers to delivery of a health assessment for preschool children [49]. More recently, Garbutt et al. used the Consolidated Framework for Implementation Research (CFIR) followed by the TDF and COM-B to identify barriers to HPV vaccine guideline implementation and designed a multipronged intervention to overcome identified barriers [20]. We aim to add to this growing body of literature by presenting in detail our methods of mapping TDF domains to the Behaviour Change Wheel to develop the KT-MCC Strategy.

The purpose of the TDF is to guide researchers to identify domains that are most likely to impact a behavior change [48]. Our key informants revealed themes related to all domains, and we therefore incurred the challenge of interpreting themes pertaining to 14 TDF domains. A review of the KT literature did not identify any strategies to identify domains that were considered most salient by participants. Moreover, we were unable to determine whether some domains were more likely to influence MCC decision making, as compared to others. Our experience likely reflects those of other researchers who use the TDF to identify barriers to complex and multifaceted behaviours. Recently, Skolarus et al. proposed the use of discrete choice experiments (DCE) to allow for the prioritization of barriers and facilitators identified using the TDF [50]. Advancement in methods such as these will facilitate the identification of the most salient domains likely to influence a behavior of interest, as defined by the target population.

To confirm the validity of our TDF-guided key informant interviews, we held focus groups with MCC participants. Our focus group participants did not identify any new mediators to MCC decision making and did not recommend any additional interventions to those proposed. This reinforces the comprehensiveness of the TDF and COM-B to develop KT interventions. We encountered our second challenge in moving from COM-B intervention functions to pragmatic KT-MCC Strategy interventions. Once again, all facets of behaviour (COM) qualified for intervention, yet we were unable to determine whether some facets would hold a greater impact on behaviour than others. We opted for a multi-pronged approach that encompassed principles of all of the Behaviour Change Wheel intervention functions. To develop the details of each KT-MCC Strategy intervention component (e.g., frequency and content of team training session), we consulted Effective Practice and Organization of Care (EPOC) systematic reviews and other pertinent literature from multidisciplinary fields. Michie et al. are aiming to streamline such processes by developing an interactive online tool to link COM-B intervention functions to mechanisms of action known as Behaviour Change Techniques (BCT). BCTs represent the individual components (i.e., details) of intervention functions [31]. Explicit linkages between COM-B intervention functions, corresponding BCTs and evidence will significantly aid implementation scientists in the practical development of theoretically-rooted interventions.

Given that the acceptability of certain KT-MCC Strategy interventions differed by MCC participants, the intervention must be adaptable to individual MCC teams. To allow for such adaptations, we will implement the KT-MCC Strategy using an iterative, integrated knowledge translation approach – members of the target population (i.e., individual MCC teams) will modify the intervention to their own context, as warranted [51]. We will use an iterative approach to further tailor and refine the intervention. We posit that this approach will likely improve adoption of, and fidelity to, selected interventions.

To the best of our knowledge, this is the first study to utilize theory to identify domains that influence physician behaviour during MCC decision making processes. In 1995, the Calman-Hine report recommended a series of reforms to reduce inequality and improve outcomes for cancer care in the United Kingdom [52]. One of the key recommendations of the report was the promotion of multidisciplinary collaboration to improve the quality of cancer care. Since then, MCCs have been mandated for use or are commonly used in most North American and European countries [1, 2, 53]. Despite the increased emphasis and reporting on MCCs, there has been little evaluation of the quality of decision making at these rounds. We identified only one other example in the literature that implemented a quality improvement intervention that aimed to improve MCC decision making. The team, led by Lamb et al., introduced an intervention comprised of an MCC checklist, team training, and written guidance to optimize MCC decision making for a urological MCC in the United Kingdom [5]. The authors noted marginal improvements in the quality of teamwork and communication attributed to their intervention [5]. As the next step of our work, we will implement and evaluate the impact of the KT-MCC Strategy on the quality of MCC decision making.

Finally, qualitative literature regarding MCC quality mediators are generally consistent with our presented findings [2, 38, 53, 54]. For instance, Look Hong et al. completed a thematic analysis of key informant interviews with MCC physicians and administrators participating in Ontario gastrointestinal MCCs [2]. The study showed that efficient management of cases by a MCC chair, identification of strategies to improve participant attendance, and recognition of radiologist and pathologist roles were critical to MCC structure. Moreover, collegiality, level of teamworking, and the availability of technological and institutional factors were likely to impact MCC functioning. Similarly, Jalil et al. conducted a qualitative study in the United Kingdom to assess MCC participants’ views on decision making [54]. Their analysis indicated that inadequate clinical information, lack of investigation results, non-attendance of key members, and teleconferencing failures were major barriers to MCC decision making. While these findings are consistent with our results, our study provides the added advantage of exploring these themes comprehensively and as theoretical domains. This approach allowed us to directly map identified barriers and facilitators to corresponding KT interventions to develop the KT-MCC Strategy.

There are limitations to the presented study. First, key informant data may not be representative of MCCs across Ontario. Our research team aimed to promote generalizability of the findings by selecting a representative sample of physicians at multiple academic and community hospitals. As well, our findings are consistent with previously published MCC literature. Thus, our barriers and facilitators are likely generalizable to other jurisdictions. Second, the face validity of the KT-MCC Strategy was evaluated among focus group participants that represented a single LHIN. We posit that the findings presented in this study are likely generalizable beyond this LHIN, due to consistencies with our key informant data as well as Ontario and international MCC literature. However, in keeping with an integrated KT approach, individual Ontario MCC teams should adapt the KT-MCC Strategy prior to implementation. Third, this study demonstrated the comprehensiveness of using both the TDF and COM-B to design an implementation strategy; however, challenges with these methods were identified. Additional methods that can be used to prioritize barriers, facilitators and implementation strategies most likely to drive behaviour are needed.

Conclusion

This is the first study to use a theoretical framework to identify barriers and facilitators to optimal MCC decision making, and to select corresponding interventions to develop a KT strategy designed to improve MCC decision making. Myriad processes influenced MCC decision making, as evidenced in our results. The developed KT-MCC Strategy included the use of workshops to identify team goals, team and chair training, standardized intake form and a synoptic discussion checklist, and audit and feedback. In the next phase of this study, we will pilot the KT-MCC Strategy to evaluate the feasibility of implementation and potential impact of the strategy on MCC decision making quality.